I-Corps: Scalable Artificial Intelligence-Supported Flood Resilience Assessment

这个I-Corps项目的更广泛的影响/商业潜力是开发一个可扩展的洪水恢复力软件框架，以在各种洪水场景和多个地理尺度下提供快速，准确和有效的洪水损失预测。拟议的网络基础设施平台提供的服务可以提供可扩展的、动态的、智能的、建筑物级的洪水复原力评估。所提出的技术大大减少了测量房屋水平最低楼层标高的工作量，并通过提供用户定义场景的即时损失预测服务，在很大程度上促进了社区水平的洪水损失评估。成功部署拟议技术的一个好处可能是帮助社区快速探索洪水风险的空间分布，并测试不同强度的洪水事件如何影响个别房屋以及整个社区。这些知识预计将进一步造福政府官员、第一反应者和资源分配者。 这些知识还将有助于提高区域和国家层面的洪水意识。这个I-Corps项目是基于开发一个人工智能支持的地理空间网络基础设施平台，用于洪水灾害预测。 现有的社区一级洪水复原力和适应能力往往是以不可扩展的方式进行调查的，这使得调查工作流程具有社区特异性，难以转移到其他社区或大的地理范围。相比之下，所提出的技术通过以下方式实现了准确、快速和低成本的洪水恢复力评估：1）使用美国国家建筑物足迹和交叉引用的洪泛区产品导出细粒度的建筑物级洪水暴露，2）提出最低楼层高程检索的可扩展工作流程，利用街景图像，3）开发一个洪水破坏模拟范例，结合建筑物特征和模拟洪水强度，以及4）设计一个可扩展的洪水恢复力评估的在线门户网站，具有交互式更新，洪水情景选择，位置查询和报告生成。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of a scalable, flood resilience software framework to provide fast, accurate, and valid flood damage prediction under various flooding scenarios and at multiple geographic scales. The services provided by the proposed cyberinfrastructure platform may provide scalable, dynamic, intelligent, building-level flood resilience assessment. The proposed technology significantly reduces the workload of measuring house-level lowest floor elevation and largely facilitates community-level flood damage assessment by providing services of on-the-fly damage predictions with user-defined scenarios. One benefit of the successful deployment of the proposed technology may be to help communities quickly explore the spatial distribution of flood risks and test how flood events with varying intensities affect individual houses as well as the whole community. Such knowledge is expected to further benefit government officials, first responders, and resource allocators. The knowledge will also help promote flood awareness at the regional and national levels.This I-Corps project is based on the development of an Artificial Intelligence (AI)-supported geospatial cyberinfrastructure platform for flood damage prediction. Existing community-level flood resilience and adaptation are often investigated in an unscalable manner, making the investigation workflow community-specific with low transferability to other communities or to large geographical scales. In comparison, the proposed technology achieves accurate, fast, and low-cost flood resilience assessment by 1) deriving fine-grained, building-level flood exposure using United States national building footprints and cross-referenced floodplain products, 2) proposing a scalable workflow of lowest floor elevation retrieval, taking advantage of street view images, 3) developing a flood damage simulation paradigm incorporating building characteristics and simulated flood intensity, and 4) designing an online portal for scalable flood resilience assessment, with the capability of interactive updates, flood scenario selection, location queries, and report generation. The proposed AI-supported cyberinfrastructure and flood damage simulation framework are expected to renovate and transform large-scale flood damage assessment and flood situational awareness communication.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.